Abstract :

Social Networking Platforms Have Become An Integral Part Of Modern Communication, Enabling Users To Share Information, Build Communities, And Interact Globally. However, The Increasing Popularity Of These Platforms Has Also Led To The Proliferation Of Fake Profiles Created For Spamming, Phishing, Misinformation Dissemination, Identity Theft, And Other Malicious Activities. These Fraudulent Accounts Pose Significant Threats To User Privacy, Platform Credibility, And Online Security. Traditional Detection Methods Often Struggle To Identify Sophisticated Fake Profiles Due To Their Evolving Behavior And Realistic Appearance. This Paper Presents A Fake Profile Identification Framework In Social Networks Using Machine Learning (ML) And Natural Language Processing (NLP) Techniques. The Proposed System Analyzes User Profile Attributes, Posting Behavior, Social Interactions, And Textual Content To Distinguish Genuine Users From Fake Accounts. NLP Techniques Are Employed To Extract Meaningful Features From User-generated Content, While Machine Learning Algorithms Classify Profiles Based On Behavioral And Linguistic Patterns. Experimental Analysis Demonstrates That The Proposed Framework Effectively Identifies Fake Profiles With High Accuracy And Reliability. The Developed System Provides An Intelligent And Scalable Solution For Enhancing Social Network Security, Protecting User Privacy, And Maintaining The Integrity Of Online Communities

Published:

26-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

425-433

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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